Hierarchical Affordance Discovery using Intrinsic Motivation

Abstract

To be capable of lifelong learning in a real-life environment, robots have to tackle multiple challenges. Being able to relate physical properties they may observe in their environment to possible interactions they may have is one of them. This skill, named affordance learning, is strongly related to embodiment and is mastered through each person's development: each individual learns affordances differently through their own interactions with their surroundings. Current methods for affordance learning usually use either fixed actions to learn these affordances or focus on static setups involving a robotic arm to be operated. In this article, we propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties. We then present one experiment and analyse our system before comparing it with other approaches from reinforcement learning and affordance learning.

7th International Conference on Human-Agent Interaction (HAI '19), Oct 2019, Kyoto, Japan

Cited by in corpus (5)

• Rediscovering Affordance: A Reinforcement Learning Perspective
• Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy
• Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning
• Autonomous learning of multiple, context-dependent tasks
• Empirical Study of Sustaining the Actualized Value Propositions of Implemented E-Government Projects in Sub-Saharan Africa